The present invention relates generally to the field of pattern recognition, and more particularly to selecting sub-sets of data associated with classes in pattern recognition in situations with noisy and/or sparse data.
The Wikipedia entry for “pattern recognition” (http://en.wikipedia.org/wiki/Pattern_recognition, as of Apr. 16, 2015) states as follows: “Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning . . . . Pattern recognition systems are in many cases trained from labeled ‘training’ data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning) . . . . Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform ‘most likely’ matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns.”
In identifying patterns and/or creating indicia of patterns to be identified, pattern recognition may employ data collected from machine based sensor hardware for various “classes.” One example of this is when sensors collect data relevant to brain activity, where the “classes” are the different brains from which the sensor data is collected.